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A BAYESIAN CLASSIFICATION APPROACH TO MONETARY AGGREGATION

Published online by Cambridge University Press:  01 April 2009

Apostolos Serletis
Apostolos Serletis*
Affiliation:
University of Calgary
*
Address correspondence to: Apostolos Serletis, Department of Economics, University of Calgary, Calgary, Alberta T2N 1N4, Canada; e-mail: serletis@ucalgary.ca.
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Abstract

In this article we use Bayesian classification and finite mixture models to extract information from the MSI database (maintained by the Federal Reserve Bank of St. Louis) and construct a new set of non-nested monetary aggregates (under the Divisia aggregation procedure) based on statistical similarities and multidimensional structures. We also use recent advances in the fields of applied econometrics, dynamical systems theory, and statistical physics to investigate the relationship between the new money measures and economic activity. The empirical results offer practical evidence in favor of this approach to monetary aggregation.

Keywords

Monetary AggregationMonetary Policy

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 13 , Issue 2 , April 2009 , pp. 200 - 219
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Andrews, D.W.K. (1991) Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59, 817–858.Google Scholar
Barnett, W.A. (1980) Economic monetary aggregates: An application of aggregation and index number theory. Journal of Econometrics 14, 11–48.Google Scholar
Barnett, W.A. (2006) Comments on “Chaotic monetary dynamics with confidence.” Journal of Macro-economics 28 253–255.CrossRefGoogle Scholar
Barnett, W.A. and Chen, P. (1988) The aggregation-theoretic monetary aggregates are chaotic and have strange attractors: An econometric application of mathematical chaos. In Barnett, W.A., Berndt, E., and White, H. (eds.), Dynamic Econometric Modeling, Proceedings of the Third International Symposium in Economic Theory and Econometrics, pp. 199–246. Cambridge: Cambridge University Press.Google Scholar
Barnett, W.A., Fisher, D., and Serletis, A. (1992) Consumer theory and the demand for money. Journal of Economic Literature 30, 2086–2119.Google Scholar
Barnett, W.A., Gallant, A.R., Hinich, M.J., Jungeilges, J.A., Kaplan, D.T., and Jensen, M.J. (1995) Robustness of nonlinearity and chaos tests to measurement error, inference method, and sample size. Journal of Economic Behavior and Organization 27, 301–320.Google Scholar
Barnett, W.A., Gallant, A.R., Hinich, M.J., Jungeilges, J.A., Kaplan, D.T., and Jensen, M.J. (1997) A single-blind controlled competition among tests for nonlinearity and chaos. Journal of Econometrics 82, 157–192.Google Scholar
Barnett, W.A. and Serletis, A. (2000a) Martingales, nonlinearity, and chaos. Journal of Economic Dynamics and Control 24, 703–724.Google Scholar
Barnett, W.A. and Serletis, A. (2000b) The Theory of Monetary Aggregation. Amsterdam: North-Holland.Google Scholar
Baxter, M. and King, R.G. (1999) Measuring business cycles: Approximate band-pass filters for economic time series. The Review of Economics and Statistics 81, 575–593.CrossRefGoogle Scholar
Dickey, D.A. and Fuller, W.A. (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057–1072.Google Scholar
Diewert, W.E. (1976) Exact and superlative index numbers. Journal of Econometrics 4, 115–146.CrossRefGoogle Scholar
Engle, R.F. and Granger, C.W. (1987) Cointegration and error correction: Representation, estimation and testing. Econometrica 55, 251–276.CrossRefGoogle Scholar
Hodrick, R.J. and Prescott, E.C. (1980) Post-War U.S. Business Cycles: An Empirical Investigation. Working paper, Carnegie Mellon University.Google Scholar
Kydland, F.E. and Prescott, E.C. (1990) Business cycles: Real facts and a monetary myth. Quarterly Review, Federal Reserve Bank of Minneapolis 3–18.Google Scholar
MacKinnon, J.G. (1994) Approximate asymptotic distribution functions for unit-root and cointegration tests. Journal of Business and Economic Statistics 12, 167–176.Google Scholar
Nychka, D.W., Ellner, S., Gallant, A.R., and McCaffrey, D. (1992) Finding chaos in noisy systems. Journal of the Royal Statistical Society B 54, 399–426.Google Scholar
Pantula, S.G., Gonzalez-Farias, G., and Fuller, W. (1994) A comparison of unit-root test criteria. Journal of Business and Economic Statistics 12, 449–459.Google Scholar
Peng, C.-K., Buldyrev, S.V., Havlin, S., Simmons, M., Stanley, H.E., and Goldberger, A.L. (1994) Mosaic organization of DNA nucleotides. Physical Review E 49, 1685–1689.Google Scholar
Phillips, P.C.B. (1987) Time series regresion with a unit root. Econometrica 55, 277–301.Google Scholar
Phillips, P.C.B. and Perron, P. (1987) Testing for a unit root in time series regression. Biometrica 75, 335–346.Google Scholar
Rotemberg, J., Driscoll, J.C., and Poterba, J.M. (1995) Money, output and prices: Evidence from a new monetary aggregate. Journal of Business and Economic Statistics 13, 67–83.Google Scholar
Serletis, A. (1995) Random walks, breaking trend functions, and the chaotic structure of the velocity of money. Journal of Business and Economic Statistics 13, 453–458.Google Scholar
Serletis, A. (2007) The Demand for Money: Theoretical and Empirical Approaches, 2nd ed.New York: Springer-Verlag.Google Scholar
Serletis, A. and Andreadis, I. (2000) Chaotic analysis of U.S. money and velocity measures. International Journal of Systems Science 31, 161–169.Google Scholar
Serletis, A. and Shintani, M. (2006) Chaotic monetary dynamics with confidence. Journal of Macro-economics 28, 228–252.CrossRefGoogle Scholar
Serletis, A. and Uritskaya, O.Y. (2007) Detecting signatures of stochastic self-organization in US money and velocity measures. Physica A 385, 281–291.Google Scholar
Shintani, M. and Linton, O. (2003) Is there chaos in the world economy? A nonparametric test using consistent standard errors. International Economic Review 44, 331–358.Google Scholar
Shintani, M. and Linton, O. (2004) Nonparametric neural network estimation of Lyapunov exponents and direct tests for chaos. Journal of Econometrics 120, 1–33.CrossRefGoogle Scholar
Stutz, J. and Cheeseman, P. (1996) AutoClass: A Bayesian approach to classification. In Skilling, J. and Sibisi, S. (eds.), Maximum Entropy and Bayesian Methods. Kluwer Academic Publishers.Google Scholar
Whang, Y.-J. and Linton, O. (1999) The asymptotic distribution of nonparametric estimates of the Lyapunov exponent for stochastic time series. Journal of Econometrics 91, 1–42.Google Scholar